Recognition of verbal commands is being used in numerous processes and devices. Numerous devices are built with microprocessors, which have small processing power, which limits the size of neural networks. The thesis presents a system for recognizing verbal commands using a convolutional neural network. The neural network was trained with patterns that will result in overfitting. The overfitting property was used to increase the recognition accuracy. A comparison of the recognition of patterns of a known speaker and patterns of an unknown speaker was performed. The comparison showed a positive overfitting property in recognition of verbal commands.
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